Staff View: Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This e...

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Main Authors:	Ab Rasid, Aina Munirah, Musa, Rabiu Muazu, Majeed, Anwar P. P. Abdul, Maliki, Ahmad Bisyri Husin Musawi, Abdullah, Mohamad Razali, Razmaan, Mohd Azraai Mohd, Abu Osman, Noor Azuan
Format:	Article
Published:	Public Library of Science 2024
Subjects:	Q Science (General) R Medicine (General) T Technology (General)
Online Access:	http://eprints.um.edu.my/45643/ https://doi.org/10.1371/journal.pone.0296467
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spelling	my.um.eprints.456432024-11-06T09:16:56Z http://eprints.um.edu.my/45643/ Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis Ab Rasid, Aina Munirah Musa, Rabiu Muazu Majeed, Anwar P. P. Abdul Maliki, Ahmad Bisyri Husin Musawi Abdullah, Mohamad Razali Razmaan, Mohd Azraai Mohd Abu Osman, Noor Azuan Q Science (General) R Medicine (General) T Technology (General) The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model's performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance. Public Library of Science 2024-02 Article PeerReviewed Ab Rasid, Aina Munirah and Musa, Rabiu Muazu and Majeed, Anwar P. P. Abdul and Maliki, Ahmad Bisyri Husin Musawi and Abdullah, Mohamad Razali and Razmaan, Mohd Azraai Mohd and Abu Osman, Noor Azuan (2024) Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis. PLoS ONE, 19 (2). e0296467. ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0296467 <https://doi.org/10.1371/journal.pone.0296467>. https://doi.org/10.1371/journal.pone.0296467 10.1371/journal.pone.0296467
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	Q Science (General) R Medicine (General) T Technology (General)
spellingShingle	Q Science (General) R Medicine (General) T Technology (General) Ab Rasid, Aina Munirah Musa, Rabiu Muazu Majeed, Anwar P. P. Abdul Maliki, Ahmad Bisyri Husin Musawi Abdullah, Mohamad Razali Razmaan, Mohd Azraai Mohd Abu Osman, Noor Azuan Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
description	The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model's performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.
format	Article
author	Ab Rasid, Aina Munirah Musa, Rabiu Muazu Majeed, Anwar P. P. Abdul Maliki, Ahmad Bisyri Husin Musawi Abdullah, Mohamad Razali Razmaan, Mohd Azraai Mohd Abu Osman, Noor Azuan
author_facet	Ab Rasid, Aina Munirah Musa, Rabiu Muazu Majeed, Anwar P. P. Abdul Maliki, Ahmad Bisyri Husin Musawi Abdullah, Mohamad Razali Razmaan, Mohd Azraai Mohd Abu Osman, Noor Azuan
author_sort	Ab Rasid, Aina Munirah
title	Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_short	Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_full	Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_fullStr	Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_full_unstemmed	Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_sort	physical fitness and motor ability parameters as predictors for skateboarding performance: a logistic regression modelling analysis
publisher	Public Library of Science
publishDate	2024
url	http://eprints.um.edu.my/45643/ https://doi.org/10.1371/journal.pone.0296467
_version_	1816130431849857024
score	13.235362

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

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